I love df

Forces"? 1.2 Is DF canon? 1.3 What is required to run DF? 1.4 What media format is DF offered on? 1.5 What sound cards are supported? 1.6 What controllers are supported? 1.7 Does DF have _________? 1.7.1 ...a multiplayer option? 1.7.2 ...a "save" feature? 1.8 How do I get DF? * 1.9 Will there be any sequels to DF? 1.10 MacDF 1.10.1 MacDF hardware requirements 1.10.2 Running custom levels on MacDF 1.10.3 MacDF patch file 1.10.4 MacDF demo 1.11 Sony PlayStation DF 2.0 DF Demo 2.1 Where can I get the demo? 2.2 Is the demo level in the full game release? 3.0 DF vs. DOOM 3.1 Isn't DF just a ripoff of iD Software's DOOM? 3.2 Does DF kick DOOM's butt? 3.3 Is DF easier than DOOM? 4.0 Undocumented Codes 4.1 Gameplay Cheat Codes 4.1.1 LABUG 4.1.2 LACDS 4.1.3 LADATA 4.1.4 LAIMLAME 4.1.5 LAMAXOUT 4.1.6 LANTFH 4.1.7 LAPOGO 4.1.8 LAPOSTAL 4.1.9 LARANDY 4.1.10 LAREDLITE 4.1.11 LASKIP 4.1.12 LAUNLOCK 4.1.13 LABRADY (Mac only) 4.1.14 LAOZ (Mac only) 4.1.15 LATERMINATE (Mac only) 4.2 Mission Codes 4.3 "Dork Farces" :) 4.4 Command Line Options 5.0 Weapons and Items 5.1 Weapons 5.1.1 Fists 5.1.2 Modified Bryar Pistol 5.1.3 Stormtrooper Laser Rifle 5.1.4 Thermal Detonators 5.1.5 Imperial Repeater Gun 5.1.6 Jeron Fusion Cutter 5.1.7 I.M. Mines 5.1.8 Packered Mortar Gun 5.1.9 Stouker Concussion Rifle 5.1.10 Assault Cannon 5.2 Ammo 5.3 Weapon Supercharge 5.4 How is aiming/targeting accomplished? 5.5 Various Items 5.5.1 Keys 5.5.2 Head Lamp 5.5.3 Infrared Goggles 5.5.4 Ice Cleats 5.5.5 Air Mask 5.5.6 Batteries 5.5.7 Shields 5.5.7.1 Shield Supercharge 5.5.7.2 Revive 5.5.7.3 Med Kit 6.0 Opponents 6.1 Imperial Stormtroopers 6.2 Imperial Officers 6.3 Imperial Commandos 6.4 Gun Turrets 6.5 Probe Droids 6.6 Interrogation Droids 6.7 Remotes 6.8 Mouse Droids 6.9 Trandoshans 6.10 Gran 6.11 Gamorreans 6.12 Dianoga 6.13 Boba Fett 6.14 Kell Dragons * 6.15 Dark Troopers 6.16 How many opponents are there in each Mission? * 6.17 How many shots are required to take out each opponent? + 6.18 How many hits can Kyle take from each weapon? 7.0 The Missions 7.1 Prologue Mission I: The Death Star Plans: Operation Skyhook: Phase 2 7.2 Mission II: After the Massacre 7.3 Mission III: The Subterranean Hideout 7.4 Mission IV: Imperial Weapons Research Facility 7.5 Mission V: The Blood Moon 7.6 Mission VI: Crix Madine's Fate 7.7 Mission VII: Deadly Cargo 7.8 Mission VIII: Ice Station Beta 7.9 Mission IX: The Death Mark 7.10 Mission X: Jabba's Revenge 7.11 Mission XI: The Imperial Mask 7.12 Mission XII: Smuggler's Hijack 7.13 Mission XIII: The Stowaway * 7.14 Mission XIV: The Dark Awakening 8.0 Miscellaneous Questions 8.1 What is "headwave"? 8.1.1 Does DF induce motion sickness? 8.2 Who are the people behind DF? 8.3 Has

George Lucas provided any input to DF? 8.4 What is the name of Kyle's dropship? 8.5 What happened to the Imperial-class Star Destroyer Mission? * 8.6 Is there a strategy guide? * 8.7 Is there a DF fan club? * 8.8 Is there any DF merchandise available? 9.0 DF Customization 9.1 How do I remap the keys? 9.2 What are .GOB files? 9.2.1 What software was used to create the .GOB files? 9.2.2 How should a custom .GOB file be loaded? 9.3 How are the different floors separated? 9.4 How are textures assigned? 9.5 How are dynamic properties assigned? 9.6 How do you place objects in a mission? 9.7 Can you create floor panels that can be blown up? 9.8 Can you create sloped floors? 9.9 How are sounds associated with actions? 9.10 Is there a limit to the size and complexity of the missions? 9.11 Day of the Tentacle? * 10.0 Custom Missions and Mission Editors * 10.1 MPC mission editors * 10.2 MacDF mission editors * 10.3 Custom missions * 11.0 Troubleshooting 11.1 Help installing 11.2 Help with sound cards * 11.3 Misc help 11.4 How do I contact LA technical support? * 12.0 Acknowledgements 13.0 References======================================================================================0.0 Welcome!============This file is a list of frequently asked questions about the MPC (MS-DOSCD-ROM) game _Star Wars: Dark Forces_ (which was released March 7, 1995). It's also a file of answers to those questions ;-) DF was voted the #1 game!(Poll taken in _Computer Gaming World_'s April, 1995 issue; results listed inthe June, 1995 issue.)*** NOTE! ***This is the 'final' version of the FAQList. I have relinquished maintainanceof the custom missions list (see section 10.3), so I do not anticipate making any changes to this FAQList (sorry, but I just don't have the time). A great big THANKS! to all who contributed. I hope this document was able to help some of you through DF!Dark Forces Project Leader Daron Stinnett and Lead Artist/Co-writer JustinChin provided me with much inside info on DF. Many of the questions belowwere answered with info provided to me by them. Thanks, Daron and Justin!The *new* WWW site for the DF FAQ is (and will be, for the time being): great big thanks to John-Paul May and the great people at TWEB for givingthe DF FAQList Website a home for over a year! Unfortunately, a fire destroyed TWEB in October, 1996. :-(I humbly note that my site has been honoured on the "Cool List" compiled byWarren Ernst , author of the excellent _Using Netscape_ .Check out his Cool List at: -KAL May the (Dark) Forces be with you!================1.0 Introduction================1.1 What is "Dark Forces"?=========================="A long time ago, in a galaxy far, far away...."The New Order of the Empire stretches

Category codesdf['Sex'] = df['Sex'].cat.codesAfter this, we need to convert the rest of the variables:# subset all categorical variables which need to be encodedcategorical = ['Embarked', 'Title']for var in categorical: df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1) del df[var]# drop the variables we won't be usingdf.drop(['Cabin', 'Name', 'Ticket', 'PassengerId'], axis=1, inplace=True)df.head()## scale continuous variablecontinuous = ['Age', 'Fare', 'Parch', 'Pclass', 'SibSp', 'Family_Size']scaler = StandardScaler()for var in continuous: df[var] = df[var].astype('float64') df[var] = scaler.fit_transform(df[var].values.reshape(-1, 1))Once preprocessing is done we need to split the train and test the dataset again, for that you can use the following code.X_train = df[pd.notnull(df['Survived'])].drop(['Survived'], axis=1)y_train = df[pd.notnull(df['Survived'])]['Survived']X_test = df[pd.isnull(df['Survived'])].drop(['Survived'], axis=1)Now is the time to define the hyperparameters and define the architecture of the ANN model.lyrs=[8]act='linear' opt='Adam'dr=0.0# set random seed for reproducibilityseed(42)set_random_seed(42)model = Sequential()# create first hidden layermodel.add(Dense(lyrs[0], input_dim=X_train.shape[1], activation=act))# create additional hidden layersfor i in range(1,len(lyrs)): model.add(Dense(lyrs[i], activation=act))# add dropout, default is nonemodel.add(Dropout(dr))# create output layermodel.add(Dense(1, activation='sigmoid')) # output layermodel.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])model = create_model()print(model.summary())after model definition, we will fit the model on our training data and would get the model insight.# train model on full train set, with 80/20 CV splittraining = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2, verbose=0)val_acc = np.mean(training.history['val_acc'])print("n%s: %.2f%%" % ('val_acc', val_acc*100))# summarize history for accuracyplt.plot(training.history['acc'])plt.plot(training.history['val_acc'])plt.title('model accuracy')plt.ylabel('accuracy')plt.xlabel('epoch')plt.legend(['train', 'validation'], loc='upper left')plt.show()Now you can use the model for predictions on test data, using the following code chunk:# calculate predictionstest['Survived'] = model.predict(X_test)test['Survived'] = test['Survived'].apply(lambda x: round(x,0)).astype('int')solution = test[['PassengerId', 'Survived']]print(solution)ConclusionArtificial neural networks (ANNs) have many applications in various industries, including medical, security/finance, government, agricultural, and defense. Researchers have mentioned several noteworthy uses of ANNs, making them powerful models that can be applied in many scenarios in artificial intelligence. ANN algorithms are particularly effective in tasks such as image recognition, natural language processing, and predictive analytics. They have the ability to learn complex patterns and relationships from data, making them invaluable tools for solving a wide range of problems in different domains.Hope you liked the article and now have a better understanding of the ANN full form in machine learning. The ANN full form, or artificial neural network, is a powerful tool in the world of AI. If you’re wondering what is ANN in machine learning, it’s a type of AI neural network that excels at pattern recognition and data analysis, enabling intelligent systems to learn and adapt from vast amounts of information.”Key TakeawaysANNs are computational models inspired by biological neural networks, capable of learning complex patterns from dataThey have diverse applications